Apache Spark and Scala Certification Training

Learning Objectives:

     Understand Big Data and its components such as HDFS. You will learn about the Hadoop Cluster Architecture and you will also get an introduction to Spark and you will get to know about the difference between batch processing and real-time processing.
Topics:
  • What is Big Data?
  • Big Data Customer Scenarios
  • Limitations and Solutions of Existing Data Analytics Architecture with Uber Use Case
  • How Hadoop Solves the Big Data Problem?
  • What is Hadoop?
  • Hadoop’s Key Characteristics
  • Hadoop Ecosystem and HDFS
  • Hadoop Core Components
  • Rack Awareness and Block Replication
  • YARN and its Advantage
  • Hadoop Cluster and its Architecture
  • Hadoop: Different Cluster Modes

Learning Objectives:

     Learn the basics of Scala that are required for programming Spark applications. You will also learn about the basic constructs of Scala such as variable types, control structures, collections such as Array, ArrayBuffer, Map, Lists, and many more.
Topics:
  • What is Scala?
  • Why Scala for Spark?
  • Scala in other Frameworks
  • Introduction to Scala REPL
  • Basic Scala Operations
  • Variable Types in Scala
  • Control Structures in Scala

Learning Objectives:

     In this module, you will learn about object-oriented programming and functional programming techniques in Scala.
Topics:
  • Functional Programming
  • Higher Order Functions
  • Anonymous Functions
  • Class in Scala
  • Getters and Setters
  • Custom Getters and Setters
  • Properties with only Getters
  • Auxiliary Constructor and Primary Constructor
  • Singletons
  • Extending a Class
  • Overriding Methods
  • Traits as Interfaces and Layered Traits

Learning Objectives:

     Understand Apache Spark and learn how to develop Spark applications. At the end, you will learn how to perform data ingestion using Sqoop.
Topics:
  • Spark’s Place in Hadoop Ecosystem
  • Spark Components & its Architecture
  • Spark Deployment Modes
  • Introduction to Spark Shell
  • Writing your first Spark Job Using SBT
  • Submitting Spark Job
  • Spark Web UI
  • Data Ingestion using Sqoop

Learning Objectives:

     Get an insight of Spark – RDDs and other RDD related manipulations for implementing business logics (Transformations, Actions and Functions performed on RDD).
Topics:
  • Challenges in Existing Computing Methods
  • Probable Solution & How RDD Solves the Problem
  • What is RDD, It’s Operations, Transformations & Actions
  • Data Loading and Saving Through RDDs
  • Key-Value Pair RDDs
  • Other Pair RDDs, Two Pair RDDs
  • RDD Lineage
  • RDD Persistence
  • WordCount Program Using RDD Concepts
  • RDD Partitioning & How It Helps Achieve Parallelization
  • Passing Functions to Spark

Learning Objectives:

     In this module, you will learn about SparkSQL which is used to process structured data with SQL queries, data-frames and datasets in Spark SQL along with different kind of SQL operations performed on the data-frames. You will also learn about the Spark and Hive integration.
Topics:
  • Need for Spark SQL
  • What is Spark SQL?
  • Spark SQL Architecture
  • SQL Context in Spark SQL
  • User Defined Functions
  • Data Frames & Datasets
  • Interoperating with RDDs
  • JSON and Parquet File Formats
  • Loading Data through Different Sources
  • Spark – Hive Integration

Learning Objectives:

     Learn why machine learning is needed, different Machine Learning techniques/algorithms, and SparK MLlib.
Topics:
  • Why Machine Learning?
  • What is Machine Learning?
  • Where Machine Learning is Used?
  • Face Detection: USE CASE
  • Different Types of Machine Learning Techniques
  • Introduction to MLlib
  • Features of MLlib and MLlib Tools
  • Various ML algorithms supported by MLlib

Learning Objectives:

     Implement various algorithms supported by MLlib such as Linear Regression, Decision Tree, Random Forest and many more.
Topics:
  • Supervised Learning – Linear Regression, Logistic Regression, Decision Tree, Random Forest
  • Unsupervised Learning – K-Means Clustering & How It Works with MLlib
  • Analysis on US Election Data using MLlib (K-Means)

Learning Objectives:

     Understand Kafka and its Architecture. Also, learn about Kafka Cluster, how to configure different types of Kafka Cluster. Get introduced to Apache Flume, its architecture and how it is integrated with Apache Kafka for event processing. At the end, learn how to ingest streaming data using flume.
Topics:
  • Need for Kafka
  • What is Kafka?
  • Core Concepts of Kafka
  • Kafka Architecture
  • Where is Kafka Used?
  • Understanding the Components of Kafka Cluster
  • Configuring Kafka Cluster
  • Kafka Producer and Consumer Java API
  • Need of Apache Flume
  • What is Apache Flume?

Learning Objectives:

     In this module, you will learn about the different streaming data sources such as Kafka and flume. At the end of the module, you will be able to create a spark streaming application.
Topics:
  • Apache Spark Streaming: Data Sources
  • Streaming Data Source Overview
  • Apache Flume and Apache Kafka Data Sources
  • Example: Using a Kafka Direct Data Source
  • Perform Twitter Sentimental Analysis Using Spark Streaming

Learning Objectives:

     Work on an end-to-end Financial domain project covering all the major concepts of Spark taught during the course.

 

Learning Objectives:

     In this module, you will be learning the key concepts of Spark GraphX programming and operations along with different GraphX algorithms and their implementations.

 

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